A standard problem in computer vision is object detection, in which the network model try to locate and identify each object present in the image. Object detection is widely used in face detection, autonomous vehicle etc. In this section, we present our results on challenging Pascal VOC dataset [1]} on Single Shot MultiBox Detector(SSD) 300 [2]} with VGG-16(with batch-normalization) [3]} as the backbone network. No pre-trained weight is considered for our experiments in the network. The network has been trained with a batch size of 8, 0.001 learning rate, SGD optimizer [4]}, [5]} with 0.9 momentum, 5\(e^{-4}\)  weight decay for 120000 iterations. We report the mean average precision (mAP) in Table REF  for a mean of 5 different runs.
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